Is Heidi really happier in the mountains? A mixed-methods investigation of spatial affect in fiction

SSentiA: A Self-supervised Sentiment Analyzer for classification from unlabeled data

The rapid development of social media makes people write their opinions on something, and make the data source for this research one of them from social media, Twitter, which will be analyzed in the form of sentiment analysis which is a process to understand and process data to get the information contained. In the opinion of the sentence. The trend of legal online loan/credit applications widely used by people in Indonesia is a hot topic discussed by the public on Twitter. If you want to know the tendency of public comments on online loan/credit applications, whether positive or negative, then sentiment analysis is carried out. The stages in conducting sentiment analysis in this study are data preprocessing, data processing, classification, and evaluation. The nave Bayes method is used because it has a high level of accuracy in classifying sentiments. Combining it with the lexicon can add precision to organizing emotions compared to other methods. The data used in this study were 4059 data with the distribution of 70% training data and 30% test data. Sentiment analysis obtained in this research shows that Twitter users in Indonesia give more negative comments. This study shows that by using both methods and testing using RapidMiner, the level of accuracy for classifying positive and negative sentiments achieves entirely satisfactory results, namely 82.06%, where the accuracy is higher than using only the lexicon-based saka method by 80%.

Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry

Multilingual sentiment analysis in healthcare is rapidly expanding, utilizing machine learning methods to identify emotions and sentiments in material written in several languages. This multidisciplinary field integrates computational linguistics, natural language processing, and health informatics to help healthcare providers better comprehend patient attitudes. Sentiment analysis is particularly useful in the healthcare industry since it facilitates comprehension of patient feedback, responses to interventions, and general contentment. Moreover, in an increasingly interconnected society, it can assist in recognizing the emotional states and concerns of patients from diverse linguistic origins. Healthcare providers can improve care and service by gaining insights into patient experiences through the analysis of patient reviews, social media posts, and other kinds of feedback in several languages. Sentiment analysis, for example, can be used to track patients' mental health over time and identify symptoms of depression or anxiety based on their interactions. These applications are becoming increasingly important for adjusting patient support and care, fostering better patient-provider communication, and eventually improving health outcomes. There are difficulties when implementing sentiment analysis in a multilingual setting, such as the requirement for extensive datasets in several languages and models that are sensitive to cultural quirks and context. By offering a foundation for creating more precise and sophisticated sentiment analysis systems that can function in a variety of linguistic and cultural contexts, advances in AI models, such as BERT and GPT variations, are assisting in addressing these issues. Recall that although sentiment analysis holds great potential, its use in healthcare needs to be done carefully to protect patient privacy and take ethical considerations into account. Sentiment analysis in healthcare can also assist in identifying unfulfilled medical and emotional demands of long-term patients, supporting patient-centred care models. In general, the incorporation of multilingual sentiment analysis into healthcare presents a multitude of opportunities and represents a promising facet of artificial intelligence's potential to enhance patient care outcomes and experiences.

With the objective of extracting useful information from the opinion-rich data on Twitter, both supervised learning-based and unsupervised lexicon-based methods for sentiment analysis on Twitter corpus have been studied in recent years. However, the unique characteristics of tweets such as the lack of labels and frequent usage of emoticons poses challenges to most of the existing learning-based and lexicon-based methods. In addition, studies on Twitter sentiment analysis nowadays mainly focus on domain specific tweets while a larger amount of tweets are about personal feelings and comments on daily life events. In this paper, a hybrid approach of augmented lexicon-based and learning-based method is designed to handle the distinctive characteristics of tweets and perform sentiment analysis on a user level, providing us information of specific Twitter users’ typing habits and their online sentiment fluctuations. Our model is capable of achieving an overall accuracy of 81.9 %, largely outperforming current baseline models on tweet sentiment analysis.

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.

In this paper we demonstrate approaches for opinion mining in Latvian text. Authors have applied, combined and extended results of several previous studies and public resources to perform opinion mining in Latvian text using two approaches, namely, semantic polarity analysis and machine learning. One of the most significant constraints that make application of opinion mining for written content classification in Latvian text challenging is the limited publicly available text corpora for classifier training. We have joined several sources and created a publically available extended lexicon. Our results are comparable to or outperform current achievements in opinion mining in Latvian. Experiments show that lexicon-based methods provide more accurate opinion mining than the application of Naive Bayes machine learning classifier on Latvian tweets. Methods used during this study could be further extended using human annotators, unsupervised machine learning and bootstrapping to create larger corpora of classified text.

Open-ended questions in patient experience surveys provide a valuable opportunity for people to express and discuss their authentic opinions. The analysis of free-text comments can add value to quantitative measures by offering information which matters most to patients and by providing detailed descriptions of the service issues that closed-ended items may not cover. To extract useful information from large amounts of free-text patient experience comments and to explore differences in patient satisfaction and loyalty between patients who provided negative comments and those who did not. We collected free-text comments on a broad, open-ended question in a cross-sectional patient satisfaction survey. We adopted a mixed-methods approach involving a literature review, human annotation, and natural language processing technique to analyze free-text comments. The associations of patient satisfaction and loyalty scores with the occurrence of certain patient comments were tested via logistic regression analysis. In total, 28054 free-text comments were collected (comment rate: 72.67%). The accuracy of the machine learning approach and the deep learning approach for topic modeling and sentiment analysis was 0.98 and 0.91 respectively, indicating a satisfactory prediction. Participants tended to leave positive comments (69.0%, 19356/28054). There were 22 patient experience themes discussed in the open-ended comments. The regression analysis showed that the occurrence of negative comments about "humanity of care", "information, communication, and education", "sense of responsibility of staff", "technical competence", "responding to requests", and "continuity of care" was significantly associated with a worse patient satisfaction and loyalty, while the occurrence of negative comments about other aspects of healthcare services had no impact on patient satisfaction and loyalty. The results of this study highlight the interpersonal and functional aspects of care, especially the interpersonal aspects, which are often the "moment of truth" during a service encounter when patients critically evaluate hospital services.

BackgroundPatients’ rights are integral to medical ethics. This study aimed to perform sentiment analysis and opinion mining on patients’ messages by a combination of lexicon-based and machine learning methods to identify positive or negative comments and to determine the different ward and staff names mentioned in patients’ messages.MethodsThe level of satisfaction and observance of the rights of 250 service recipients of the hospital was evaluated through the related checklists by the evaluator. In total, 822 Persian messages, composed of 540 negative and 282 positive comments, were collected and labeled by the evaluator. Pre-processing was performed on the messages and followed by 2 feature vectors which were extracted from the messages, including the term frequency–inverse document frequency (TFIDF) vector and a combination of the multifeature (MF) (a lexicon-based method) and TFIDF (MF + TFIDF) vectors. Six feature selectors and 5 classifiers were used in this study. For the evaluations, 5-fold cross-validation with different metrics including area under the receiver operating characteristic curve (AUC), accuracy (ACC), F1 score, sensitivity (SEN), specificity (SPE) and Precision-Recall Curves (PRC) were reported. Message tag detection, which featured different hospital wards and identified staff names mentioned in the study patients’ messages, was implemented by the lexicon-based method.ResultsThe best classifier was Multinomial Naïve Bayes in combination with MF + TFIDF feature vector and SelectFromModel (SFM) feature selection (ACC = 0.89 ± 0.03, AUC = 0.87 ± 0.03, F1 = 0.92 ± 0.03, SEN = 0.93 ± 0.04, and SPE = 0.82 ± 0.02, PRC-AUC = 0.97). Two methods of assessment by the evaluator and artificial intelligence as well as survey systems were compared.ConclusionOur results demonstrated that the lexicon-based method, in combination with machine learning classifiers, could extract sentiments in patients’ comments and classify them into positive and negative categories. We also developed an online survey system to analyze patients’ satisfaction in different wards and to remove conventional assessments by the evaluator.

Sentiment analysis is widely used to identify emotions and attitudes in text. With the growing popularity of audio-based social platforms and the significant rise in spoken data, sentiment analysis in the auditory domain has become increasingly important. This paper explores sentiment analysis in audio data using Natural Language Processing (NLP) techniques. We propose a novel method for extracting linguistic features and developing emotional models tailored to audio-based sentiment. In our experiments, we compare deep learning models with traditional NLP techniques, using a unique dataset to validate our findings. Sentiment analysis, also known as opinion mining, is a key subfield of NLP, focusing on extracting subjective information from textual data. The surge of user-generated content on online platforms like social media, blogs, and product reviews has amplified the importance of sentiment analysis for understanding public opinion and consumer behavior. This paper provides an overview of various approaches used in sentiment analysis, including machine learning, lexicon-based methods, and deep learning, highlighting their strengths and limitations. We discuss the trade-offs between accuracy, computational efficiency, and interpretability for each approach, while addressing challenges like sarcasm detection, context dependency, and domain-specific language. Additionally, we examine recent advancements in the field, such as the use of cross-sectional models and the integration of multiple data sources to provide a more comprehensive view of sentiment. Our results demonstrate the efficiency and high performance of the proposed models in capturing sentiment from audio data. The study also explores the ethical considerations, practical applications, and broader relevance of audio-based sentiment analysis across media and other domains. Finally, we conclude by discussing future directions, emphasizing the need for more robust models capable of handling diverse and complex data, along with ethical considerations for real-world applications.

Sentiment analysis refers to the algorithmic extraction of subjective information from textual data and—driven by the increasing amount of online communication—has become one of the fastest growing research areas in computer science with applications in several domains. Although sports events such as football matches are accompanied by a huge public interest and large amount of related online communication, social media analysis in general and sentiment analysis in particular are almost unused tools in sports science so far. The present study tests the feasibility of lexicon-based tools of sentiment analysis with regard to football-related textual data on the microblogging platform Twitter. The sentiment of a total of 10,000 tweets with reference to ten top-level football matches was analyzed both manually by human annotators and algorithmically by means of publicly available sentiment analysis tools. Results show that the general sentiment of realistic sets (1000 tweets with a proportion of 60% having the same polarity) can be classified correctly with more than 95% accuracy. The present paper demonstrates that sentiment analysis can be an effective and useful tool for sports-related content and is intended to stimulate the increased use of and discussion on sentiment analysis in sports science.

Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan is a legal entity that provides social health insurance programs for the public released application called Mobile JKN to support various health services activities using users devices. Mobile JKN has not fully received a positive public perception and still has many shortcomings. It is necessary to conduct a deeper evaluation and analysis of the Mobile JKN. This study focuses on aspect-based sentiment analysis of user reviews on the Google Play Store to evaluate the Mobile JKN. The review data used are the last two versions, 4.2.3 and 4.3.0. This study was carried out by modeling aspects/topics using the Latent Dirichlet Allocation method and sentiment analysis using Naïve Bayes and Lexicon-Based methods. This research resulted in 3 aspects, namely Services and Features, Register and Login, and User Satisfaction. This was obtained based on the model with the highest coherence score of 0.6392 obtained in the model looping with the number of topics from 1 to 9, random state = 42, passes =50, and iteration = 60. Meanwhile, based on the sentiment analysis results, the Naïve Bayes method is better than the Lexicon-Based (Inset Lexicon) method. This is evident from performance of the Naïve Bayes with the highest accuracy score of 94.75% and Lexicon Based with Inset Lexicon obtained an accuracy score of 59.99%.

Sentiment analysis has attracted a wide range of attentions in the last few years. Supervised-based and lexicon-based methods are two mainly sentiment analysis categories. Supervised-based approaches could get excellent performance with sufficient tagged samples, while the acquisition of sufficient tagged samples is difficult to implement in some cases. Lexicon-based method can be easily applied to variety domains but excellent quality lexicon is needed, otherwise it will get unsatisfactory performance. In this paper, a hybrid supervised review sentiment analysis method which takes advantage of both of the two categories methods is proposed. In training phrase, lexicon-based method is used to learn confidence parameters which used to determine classifier selection from a small-scale labeled dataset. Then training set which is used to train a Naive Bayes sentiment classifier. Finally, a sentiment analysis framework consist of the lexicon-based sentiment polarity classifier and the learned Naive Bayes classifier is constructed. The optimal hybrid classifier is obtained by obtaining the optimal threshold value. Experiments are conducted on four review datasets.

Cross-ratio uninorms as an effective aggregation mechanism in sentiment analysis

论可推广性的情感分析法以创建酒店字典：以TripAdvisor酒店评论为样本的大数据分析摘要研究目的对于在线游客评论的研究在过去的几年中与日俱增, 但是仍缺乏有效方法能在有限的时间喝预算内提供终端用户价值。本论文开发并测试了一套情感分析的新方法, 创建两套酒店相关的词库, 此方法超越了标准词典式分析法。研究设计/方法/途径研究样本为TripAdvisor酒店客户评论的大数据, 通过开发崭新的有配重的词库法, 来开展两极式情感分析。这个崭新的具有配重的词库法能够呈现透明化和可复制的程序, 准备、创建、并检验情感分析的词条。这个方法用到了两种词典（有配重的词典L1和手动选择的词典L2）, 本论文通过对TripAdvisor大数据进行使用词类划分精准度, 来检测和验证这两种词典。本论文采用两种热门方法（公共词典法和复杂机器学习算法）来对比词典的准确度。研究结果精确度对比结果证实了本论文的方法, 相较于机器学习算法, 显著地超越了以字典为基础的方法。研究结果还表明, 本论文的方法可以就预测用户情感趋势进行推广。研究实际启示本论文开发并验证了一项方法, 这种方法通过创建可信的词典进行大数据分析, 以判定用户情感。本论文创建的L2酒店词库对分析客人反馈是可靠有用的工具, 这个词库还能帮助酒店经理了解、预测、以及积极相应客人的态度和改变。本论文还提出了一项可以了解每个用户情感的简易方法, 这项方法可以通过对比的方式来检测和了解客人不同时间的情感变化, 以及根据其不同背景和经历的不同用户之间的变化。研究原创性/价值本论文提出并检测了一项新方法, 这项情感分析方法可以解决之前方法的局限并立脚于旅游行业。基于文献综述, 本论文是首篇研究, 使用词库法来进行情感分析和创建特别领域词典的方式。